{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "47c78979",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b93261d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a57f63cf",
   "metadata": {},
   "source": [
    "The data needed for this figure is the relative mutational signature assignment results for the subclonal and clonal mutations. The original VCF files are split based on DPClust output results, and the samples are filtered as described in the paper. This data is stored in **sigs_normed**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1ad90f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the data and normalize it\n",
    "\n",
    "sigs_normed = pd.read_table('ExtendedDataFig10_input.txt',index_col=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "596ac683",
   "metadata": {},
   "outputs": [],
   "source": [
    "# update the names to use those used in the paper\n",
    "\n",
    "sigs_normed_newnames_mapping = pd.Series(index = sigs_normed.columns,data=sigs_normed.columns)\n",
    "\n",
    "sigs_normed_newnames_mapping.loc['SBS22'] = 'SBS22a'\n",
    "sigs_normed_newnames_mapping.loc['SBS1536I'] = 'SBS22b'\n",
    "\n",
    "\n",
    "sigs_normed_newnames_mapping.loc['SBS1536A'] = 'SBS40b'\n",
    "sigs_normed_newnames_mapping.loc['SBS1536B'] = 'SBS40a'\n",
    "sigs_normed_newnames_mapping.loc['SBS1536F'] = 'SBS40c'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dcd5ef18",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBS1\n",
      "WilcoxonResult(statistic=1319.0, pvalue=2.889370917459252e-06)\n",
      "\n",
      "SBS2\n",
      "WilcoxonResult(statistic=2.0, pvalue=0.1380107375686596)\n",
      "\n",
      "SBS4\n",
      "WilcoxonResult(statistic=3855.0, pvalue=0.13823746926183894)\n",
      "\n",
      "SBS5\n",
      "WilcoxonResult(statistic=895.0, pvalue=0.33220194306235573)\n",
      "\n",
      "SBS12\n",
      "WilcoxonResult(statistic=10.0, pvalue=0.0229090993543566)\n",
      "\n",
      "SBS13\n",
      "WilcoxonResult(statistic=1.0, pvalue=4.61253801792603e-05)\n",
      "\n",
      "SBS18\n",
      "WilcoxonResult(statistic=29.0, pvalue=2.2926543090370475e-08)\n",
      "\n",
      "SBS21\n",
      "WilcoxonResult(statistic=0.0, pvalue=0.31731050786291415)\n",
      "\n",
      "SBS22\n",
      "WilcoxonResult(statistic=50.0, pvalue=0.5700608835629815)\n",
      "\n",
      "SBS44\n",
      "WilcoxonResult(statistic=0.0, pvalue=0.31731050786291415)\n",
      "\n",
      "SBS1536A\n",
      "WilcoxonResult(statistic=3757.0, pvalue=4.856552726661516e-06)\n",
      "\n",
      "SBS1536B\n",
      "WilcoxonResult(statistic=7887.0, pvalue=0.00014213979264901067)\n",
      "\n",
      "SBS1536F\n",
      "WilcoxonResult(statistic=716.0, pvalue=0.005231353515988661)\n",
      "\n",
      "SBS1536I\n",
      "WilcoxonResult(statistic=61.0, pvalue=0.010995017215526998)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SBS1        2.022560e-05\n",
       "SBS2        1.935325e-01\n",
       "SBS4        1.935325e-01\n",
       "SBS5        3.577559e-01\n",
       "SBS12       4.009092e-02\n",
       "SBS13       1.614388e-04\n",
       "SBS18       3.209716e-07\n",
       "SBS21       3.577559e-01\n",
       "SBS22       5.700609e-01\n",
       "SBS44       3.577559e-01\n",
       "SBS1536A    2.266391e-05\n",
       "SBS1536B    3.979914e-04\n",
       "SBS1536F    1.220649e-02\n",
       "SBS1536I    2.199003e-02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get correct p-values from a paired test\n",
    "\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "pvals = pd.Series(index = sigs_normed.columns)\n",
    "\n",
    "inboth_main_after_filtering = sigs_normed.index[sigs_normed.index.str.contains('main')].sort_values()\n",
    "inboth_sub_after_filtering = sigs_normed.index[sigs_normed.index.str.contains('sub')].sort_values()\n",
    "\n",
    "inboth_after_filtering  = inboth_main_after_filtering.str.split('_').str[0]\n",
    "\n",
    "for signature in sigs_normed.columns:\n",
    "    print(signature)\n",
    "    print(stats.wilcoxon(sigs_normed.loc[inboth_main_after_filtering,signature],sigs_normed.loc[inboth_sub_after_filtering,signature]))\n",
    "    pvals[signature] = (stats.wilcoxon(sigs_normed.loc[inboth_main_after_filtering,signature],sigs_normed.loc[inboth_sub_after_filtering,signature])[1])\n",
    "    print()\n",
    "\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "\n",
    "# now we correct our p-values\n",
    "pvals_corrected = pd.Series(multipletests(pvals,method='fdr_bh')[1],index =pvals.index )\n",
    "pvals_corrected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f13bc4d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sortedd_sigs = sigs_normed_newnames_mapping.loc[['SBS1', 'SBS2', 'SBS4', 'SBS5', 'SBS12', 'SBS13', 'SBS18', 'SBS21',\n",
    "       'SBS22',  'SBS1536I', 'SBS1536B','SBS1536A',  'SBS1536F','SBS44']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "70a6e06b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 518.74x481.89 with 28 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# try to make figure plot V5\n",
    "\n",
    "mm = 1/25.4  # centimeters in inches\n",
    "\n",
    "from scipy.stats import fisher_exact\n",
    "\n",
    "aa = 0\n",
    "bb = 0\n",
    "\n",
    "fig = plt.figure(constrained_layout=True,figsize=(183*mm, 170*mm))\n",
    "subplots = fig.subfigures(7,2)\n",
    "\n",
    "\n",
    "ax0 = subplots[0][0].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "ax1 = subplots[0][1].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "ax2 = subplots[1][0].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "ax3 = subplots[1][1].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "\n",
    "ax4 = subplots[2][0].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "ax5 = subplots[2][1].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "ax6 = subplots[3][0].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "ax7 = subplots[3][1].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "\n",
    "ax8 = subplots[4][0].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "ax9 = subplots[4][1].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "ax10 = subplots[5][0].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "ax11 = subplots[5][1].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "\n",
    "ax12 = subplots[6][0].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "ax13 = subplots[6][1].subplots(1,2,gridspec_kw={'width_ratios': [3, 1]})\n",
    "\n",
    "\n",
    "axxs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7,ax8,ax9,ax10,ax11,ax12,ax13]\n",
    "ax_count = 0\n",
    "\n",
    "# want to make a panel of plots for each signature\n",
    "\n",
    "\n",
    "# sort by the new sigs\n",
    "for sigg in sortedd_sigs.index:\n",
    "    curr = axxs[ax_count]\n",
    "    \n",
    "    #fig,ax = plt.subplots(1,2,figsize=(10,5),gridspec_kw={'width_ratios': [3, 1]})\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "#     ### scatter plot ###\n",
    "    mm = sigs_normed.loc[list(inboth_main_after_filtering)]\n",
    "    mm.index = mm.index.str.split('_').str[0]\n",
    "    \n",
    "    ss = sigs_normed.loc[list(inboth_sub_after_filtering)]\n",
    "    ss.index = ss.index.str.split('_').str[0]\n",
    "#     curr[0].plot([0, 1], [0, 1],color='black')\n",
    "    \n",
    "#     diff = mm.loc[mm.index,sigg] - ss.loc[mm.index,sigg]\n",
    "#     higher_in_main = diff[diff>.06].index\n",
    "    \n",
    "#     diff =ss.loc[mm.index,sigg] - mm.loc[mm.index,sigg]\n",
    "#     higher_in_sub = diff[diff>.06].index\n",
    "\n",
    "    \n",
    "#     others = (set(diff.index) - set(higher_in_main)) - set(higher_in_sub)\n",
    "    \n",
    "#     curr[0].scatter(mm.loc[others,sigg],ss.loc[others,sigg],color='gray',alpha=.2,label='no change')\n",
    "#     curr[0].scatter(mm.loc[higher_in_main,sigg],ss.loc[higher_in_main,sigg],color='blue',label='more active in clone',alpha=.5)\n",
    "#     curr[0].scatter(mm.loc[higher_in_sub,sigg],ss.loc[higher_in_sub,sigg],color='red',label='more active in subclone',alpha=.5)\n",
    "\n",
    "\n",
    "#     curr[0].set_xlabel('main clone relative activity')\n",
    "#     curr[0].set_ylabel('subclone relative activity')\n",
    "#     curr[0].set_xlim(-.1,1.1)\n",
    "#     curr[0].set_ylim(-.1,1.1)\n",
    "#     curr[0].legend(frameon=False)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    ### change plot ###\n",
    "    box = pd.DataFrame(sigs_normed.loc[list(inboth_main_after_filtering)+list(inboth_sub_after_filtering),sigg])\n",
    "    mm = sigs_normed.loc[list(inboth_main_after_filtering)]\n",
    "    mm.index = mm.index.str.split('_').str[0] \n",
    "    ss = sigs_normed.loc[list(inboth_sub_after_filtering)]\n",
    "    ss.index = ss.index.str.split('_').str[0]\n",
    "    \n",
    "    diff = mm.loc[mm.index,sigg] - ss.loc[mm.index,sigg]\n",
    "    higher_in_main = diff[diff>.06].index\n",
    "    \n",
    "    diff =ss.loc[mm.index,sigg] - mm.loc[mm.index,sigg]\n",
    "    higher_in_sub = diff[diff>.06].index\n",
    "\n",
    "    others = (set(diff.index) - set(higher_in_main)) - set(higher_in_sub)\n",
    "    \n",
    "    \n",
    "    main_name = 'main'#\\n' + str((box[box.index.str.contains('main')]>0).sum().values[0]) + '/' + str(len(box[box.index.str.contains('main')].index))\n",
    "    \n",
    "    sub_name = 'sub'#\\n' + str((box[box.index.str.contains('sub')]>0).sum().values[0]) + '/' + str(len(box[box.index.str.contains('sub')].index))\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    i=0\n",
    "    for x in others:\n",
    "        label=''\n",
    "        if i == 0:\n",
    "            label='no change'\n",
    "        curr[0].plot([main_name,sub_name],[box.loc[x + '_main'],box.loc[x+'_sub']],alpha=.3,color='gray',label=label)\n",
    "        i+=1\n",
    "    i=0    \n",
    "    for x in higher_in_main:\n",
    "        label=''\n",
    "        if i == 0:\n",
    "            label='higher in main clone'\n",
    "        curr[0].plot([main_name,sub_name],[box.loc[x + '_main'],box.loc[x+'_sub']],alpha=.5,color='blue',label=label)\n",
    "        i+=1\n",
    "    i=0    \n",
    "    for x in higher_in_sub:\n",
    "        label=''\n",
    "        if i == 0:\n",
    "            label='higher in subclone'\n",
    "        curr[0].plot([main_name,sub_name],[box.loc[x + '_main'],box.loc[x+'_sub']],alpha=.5,color='red',label=label)\n",
    "        i+=1\n",
    "    \n",
    "    \n",
    "    \n",
    "    curr[0].scatter([main_name] * len(inboth_main_after_filtering),\n",
    "                 sigs_normed.loc[inboth_main_after_filtering,sigg],color='black',s=5)\n",
    "    \n",
    "    curr[0].scatter([sub_name] * len(inboth_sub_after_filtering),\n",
    "                 sigs_normed.loc[inboth_sub_after_filtering,sigg],color='black',s=5)\n",
    "    \n",
    "    curr[0].set_ylim(-.1,1.1)\n",
    "    \n",
    "    \n",
    "    \n",
    "    #curr[1].xlabel('main -> sub')\n",
    "    if ax_count % 2 == 0:\n",
    "        curr[0].set_ylabel('relative activity')\n",
    "\n",
    "\n",
    "    #curr[0].legend(frameon=False,fontsize=14)\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    ### bar plot ###\n",
    "    # maybe do this of the ones that are not zero? Unclear, need to ask\n",
    "    greater_than_0_main = sigs_normed.loc[inboth_main_after_filtering,sigg] > 0\n",
    "    greater_than_0_sub = sigs_normed.loc[inboth_sub_after_filtering,sigg] > 0\n",
    "    greater_than_0_main.index = greater_than_0_main.index.str.split('_').str[0]\n",
    "    greater_than_0_sub.index = greater_than_0_sub.index.str.split('_').str[0]\n",
    "    above_0_both = greater_than_0_main + greater_than_0_sub\n",
    "    above_0_both=above_0_both[above_0_both]\n",
    "\n",
    "    #curr[0].set_title(sigg + ' (pvalue: ' + '{:0.2e}'.format(pvals_corrected.loc[sigg]) + ')' + ' (# nonzero samples: ' + str(len(above_0_both)) + ')')\n",
    "    subplots[aa,bb].suptitle(sigs_normed_newnames_mapping.loc[sigg] + ' (q = ' + '{:0.2e}'.format(pvals_corrected.loc[sigg]) + ')' + ' (' + str(len(above_0_both)) + '/' + str(len(inboth_after_filtering)) + ')',\n",
    "                            fontsize=7)\n",
    "    \n",
    "    if bb == 1:\n",
    "        aa+=1\n",
    "        bb =0\n",
    "    else:\n",
    "        bb+=1\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    y_main = sigs_normed.loc[above_0_both.index + '_main',sigg].mean()\n",
    "    y_sub = sigs_normed.loc[above_0_both.index + '_sub',sigg].mean()\n",
    "\n",
    "    main_std = sigs_normed.loc[above_0_both.index + '_main',sigg].std()\n",
    "    sub_std = sigs_normed.loc[above_0_both.index + '_sub',sigg].std()\n",
    "\n",
    "\n",
    "    curr[1].bar(['main','sub'],[y_main,y_sub],yerr=[main_std,sub_std],color=['blue','red'],alpha=.2)\n",
    "    \n",
    "    \n",
    "    temp_data = pd.concat([sigs_normed.loc[above_0_both.index + '_main'],\n",
    "                          sigs_normed.loc[above_0_both.index + '_sub']])\n",
    "    \n",
    "    temp_data['category'] = temp_data.index.str.split('_').str[1]\n",
    "    \n",
    "    \n",
    "#     sns.boxplot(data=temp_data,\n",
    "#                x='category',\n",
    "#                y=sigg,ax=curr[1],palette=['blue','red'],fliersize=2,\n",
    "#                saturation=.2)#,color='black',alpha=.5,s=2)\n",
    "    \n",
    "    sns.swarmplot(data=temp_data,\n",
    "               x='category',\n",
    "               y=sigg,ax=curr[1],color='black',s=2,alpha=.5)\n",
    "    \n",
    "    curr[1].set_xlabel('')\n",
    "    curr[1].set_ylabel('')\n",
    "    \n",
    "    \n",
    "    #curr[1].set_ylabel('relative activity')\n",
    "    curr[1].set_ylim(-.1,1.1)\n",
    "    \n",
    "    \n",
    "    for a in curr:\n",
    "        a.spines['top'].set_visible(False)\n",
    "        a.spines['right'].set_visible(False)\n",
    "   \n",
    "\n",
    "\n",
    "    if ax_count < 12:\n",
    "        curr[0].axes.xaxis.set_ticklabels([])\n",
    "        curr[1].axes.xaxis.set_ticklabels([])\n",
    "    if ax_count%2 != 0:\n",
    "        curr[0].axes.yaxis.set_ticklabels([])\n",
    "        curr[1].axes.yaxis.set_ticklabels([])\n",
    "    else:\n",
    "        curr[1].axes.yaxis.set_ticklabels([])\n",
    "        \n",
    "    curr[0].axes.yaxis.set_ticks([0. , 0.5, 1. ])\n",
    "        \n",
    "    for a in curr:\n",
    "        for item in ([a.title, a.xaxis.label, a.yaxis.label] + a.get_xticklabels() + a.get_yticklabels()):\n",
    "            item.set_fontsize(7)\n",
    "    \n",
    "    if ax_count == 6:\n",
    "        curr[0].set_ylabel('relative activity',fontsize=7)\n",
    "    else:\n",
    "        curr[0].set_ylabel(' ',fontsize=7)\n",
    "        \n",
    "    \n",
    "    \n",
    "    if ax_count == 0:\n",
    "        curr[0].legend(fontsize=7,frameon=False)\n",
    "    ax_count +=1\n",
    "    #plt.tight_layout()\n",
    "\n",
    "    #plt.savefig('plots_RCC_final_sigs_no_assumption_UPDATED/' + sigg + '.png',facecolor='white')\n",
    "# plt.savefig('main_sub_figure_v5.svg',facecolor='white')    "
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